In several applications there is a need for identifying digital images depicting a specific object as depicted in a specific digital image. If the specific object depicted on the reference image is a car having a registration number plate, this may be relatively straightforward to achieve, for example, using OCR techniques, whereas achieving the same for human beings or cats and the like is far more challenging, historically having left such operations to be performed manually.
One particular area where such methods are of interest is for camera surveillance systems. If a digital image shows a person, the method may be used to locate one or more images showing an object likely to be that person. For a camera surveillance system, such a method may for example be applicable for finding out if the presence of a specific object has been detected before. For example, if a crime is committed and an alleged criminal is depicted in a digital image, an operator of the camera surveillance system may click on the object showing the alleged criminal when viewing a stored video stream. A query may then be submitted such as to locate a set of candidate digital images showing what is likely to depict the alleged criminal. Additionally, metadata pertaining to the candidate digital images may be presented. Such metadata may be for example the time, date and place at which a candidate digital image was taken. From this data, it may be possible to find out if the alleged criminal was found investigating the area of the crime in advance and/or was previously seen at another area covered by the camera surveillance system.
One way of achieving such a method is to make use of deep learning algorithms using convolutional neural networks (CNNs) to teach a computer algorithm how to determine an object identity. However, such state-of-the-art methods are often very computationally intensive and are therefore often limited to a particular class of objects (such as persons, cars, cats, trees and the like) for which the CNN was pre-trained. Often it is of interest to be able to locate objects within different classes using the same digital images. Thus, there is a need in the art for an improved method which provides a faster and more accurate identification and in particular is configured for achieving identification within multiple classes of objects.